Since 2014
Unobserved Heterogeneity in Regression Models: A Semiparametric Approach Based on Nonlinear Sieves
Brazilian Review of Econometrics
View Archive Info| Field | Value | |
| Title |
Unobserved Heterogeneity in Regression Models: A Semiparametric Approach Based on Nonlinear Sieves
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| Creator |
Medeiros, Marcelo C; PUC-Rio
Burity, Priscilla Assunção, Juliano |
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| Subject |
Semiparametric models;sieve extremum estimators; neural networks;convergence;unobserved components.
C14 |
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| Description |
This paper proposes a semiparametric approach to control for unobserved heterogeneity in linear regression models, based on an artificial neural network extremum estimator. We present a procedure to specify the model and use simulations to evaluate its finite sample properties in comparison to alternative methods. The simulations show that our approach is less sensitive to increases in the dimensionality and complexity of the problem. We also use the model to study convergence of per capita income across Brazilian municipalities.
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| Publisher |
Sociedade Brasileira de Econometria
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| Contributor |
CNPq
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| Date |
2015-10-05
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion — — |
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| Format |
application/pdf
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| Identifier |
http://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/24305
10.12660/bre.v35n12015.24305 |
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| Source |
Brazilian Review of Econometrics; Vol 35, No 1 (2015); 47-63
Brazilian Review of Econometrics; Vol 35, No 1 (2015); 47-63 1980-2447 |
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| Language |
eng
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| Relation |
http://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/24305/44460
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| Rights |
Copyright (c) 2015 Brazilian Review of Econometrics
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